- In full:
- John Joseph Hopfield
- Born:
- July 15, 1933, Chicago, Illinois, U.S. (age 93)
- Awards And Honors:
- Nobel Prize (2024)
John J. Hopfield
-
Who is John J. Hopfield?
John J. Hopfield, born in Chicago, Illinois, on July 15, 1933, is an American physicist renowned for his contributions to neural networks, earning him the 2024 Nobel Prize in Physics, which he shared with Geoffrey Hinton.
Hopfield's early interest in physics was nurtured by his parents, both physicists, fostering an approach to understanding the world through experimentation and quantitative analysis. He earned a bachelor’s degree in physics from Swarthmore College in 1954 and a Ph.D. in physics from Cornell University in 1958. His career includes research at AT&T Bell Laboratories, professorships at Princeton University and the California Institute of Technology (Caltech), and his work led to the creation of the Hopfield network, a model for understanding memory storage in the brain.
1 Britannica SourceCreated from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles. -
What is John J. Hopfield known for?
John J. Hopfield is an American physicist known for his work with neural networks. In 1982, he proposed the Hopfield network, a simple network that explains how memories are stored in the brain. By drawing an analogy to the mathematics describing magnetic systems, Hopfield described an "energy" for the system, with the strength of connection set so memories would be in the system's lowest energy state.
Hopfield's work extends to more complex features, such as neurons existing in any state, not just 0 and 1, enabling the storage of more complex information. Together with David Tank, Hopfield used such a network to solve the traveling salesman problem. In 1986, Hopfield and Carver Andress Mead created a program in computation and neural systems at Caltech.
1 Britannica SourceCreated from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles. -
What is the Hopfield network?
The Hopfield network is a simple network proposed by John Hopfield in 1982 to explain how memories are stored in the brain. In this model, neurons exist in one of two states: 0, meaning not firing, or 1, meaning firing at maximum rate. The connections between neurons have a certain strength. Hopfield described an "energy" for the system mathematically, ensuring memories reside in the system's lowest energy state. He later expanded the model to store more complex information, allowing neurons to exist in any state, not just 0 or 1. Hopfield and American neuroscientist David Tank used such a network to solve the traveling salesman problem.
1 Britannica SourceCreated from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles. -
How did the Hopfield network help connect physics and neuroscience?
John J. Hopfield, an American physicist and Nobel laureate, significantly contributed to connecting physics and neuroscience through his neural network models. In 1982, Hopfield introduced a simple network model to elucidate memory storage in the brain, drawing an analogy to the mathematics describing magnetic systems.
His model, known as the Hopfield network, described neurons as existing in one of two states: either firing at maximum rate (1) or not firing (0). The connections linking neurons had a certain strength. Hopfield expanded this model to accommodate more complex features, enabling the storage of more complex information. Together with neuroscientist David Tank, Hopfield used the network to solve the traveling salesman problem.
1 Britannica SourceCreated from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles. -
What other scientific fields did John J. Hopfield contribute to?
John J. Hopfield initially focused on solid-state physics research at AT&T Bell Laboratories after earning his Ph.D. in physics from Cornell University in 1958. By the late 1960s, he transitioned from physics to chemistry and biology, becoming a professor at the California Institute of Technology (Caltech) in 1980.
Hopfield's interest in how the brain works led him to propose the Hopfield network in 1982, a model for understanding memory storage in the brain. He and Carver Andress Mead created a program in computation and neural systems at Caltech in 1986. In 1997, Hopfield returned to Princeton as a professor of molecular biology and helped establish the Princeton Neuroscience Institute.
1 Britannica SourceCreated from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles. -
How has John J. Hopfield’s work influenced modern science and technology?
John J. Hopfield is an American physicist celebrated for his contributions to the field of neural networks. His most influential work, the Hopfield network, proposed in 1982, offered a simplified model for understanding memory storage in the brain. By drawing parallels with the mathematics of magnetic systems, Hopfield described the energy of the system, where neurons exist in binary states (firing or not firing), and the connections between them dictate the strength of memory.
Hopfield's model has been extended to accommodate more complex features, enabling the storage of more intricate information. Together with David Tank, Hopfield demonstrated the network's problem-solving capabilities by using it to solve the traveling salesman problem. His interdisciplinary work led to the creation of a program in computation and neural systems at Caltech in 1986 and the Princeton Neuroscience Institute in 1997.
1 Britannica SourceCreated from Britannica articles using AI. AI can make mistakes, so verify using Britannica articles.
John J. Hopfield (born July 15, 1933, Chicago, Illinois, U.S.) is an American physicist who was awarded the 2024 Nobel Prize in Physics for his work with neural networks. He shared the prize with British-Canadian cognitive psychologist Geoffrey Hinton. At age 91, he became the third oldest person to receive a Nobel Prize, after John B. Goodenough (in chemistry in 2019, at age 97) and Arthur Ashkin (in physics in 2018, at age 96).
Hopfield, born to parents who were physicists, took an interest in the subject from an early age; he would later describe physics as not just a subject matter but rather a way in which to view the physical world. His parents encouraged his inquisitive nature, and he was allowed to take apart various items in his home, which would sometimes necessitate his father’s help in piecing them back together.
“The central idea was that the world is understandable, that you should be able to take anything apart, understand the relationships between its constituents, do experiments, and on that basis be able to develop a quantitative understanding of its behavior.” —Hopfield describing how he viewed physics
In 1954 Hopfield received a bachelor’s degree in physics at Swarthmore College. Four years later he received a Ph.D. in physics from Cornell University. Hopfield then joined AT&T Bell Laboratories, where he worked on solid-state physics research. Starting in 1964, he was a professor of physics at Princeton University. By the end of his time there, Hopfield had moved away from physics to problems in chemistry and biology, and he then became a professor of those subjects at the California Institute of Technology (Caltech) in 1980.
Hopfield became interested in how the brain works and, specifically, how neurons work together. In 1982 he proposed a simple network that would explain how memories are stored in the brain. Neurons could be in either one of two states: 0, “not firing,” or 1, “firing at maximum rate.” The connections between the neurons had a certain strength. By analogy with the mathematics that describes magnetic systems, Hopfield was able to describe an “energy” for the system that was −1 times a sum over all the pairs of neurons with the terms of the sum being the strength of the connection between two neurons times the value of the state of each neuron. The strength of the connection term was set so that memories would be in the lowest energy state of the system.
Hopfield extended his model (which came to be called the Hopfield network) to have more complex features. For example, the neurons could be in any state, not just 0 and 1. Thus, the model could store more complex information. Hopfield and American neuroscientist David Tank used such a network to solve the traveling salesman problem.
At Caltech, Hopfield and his colleague Carver Andress Mead created a new interdisciplinary program in computation and neural systems in 1986. Hopfield returned to Princeton, as a professor of molecular biology, in 1997 and there helped create the Princeton Neuroscience Institute. He retired and became a professor emeritus in 2008.
Hopfield has won multiple awards and held many prestigious positions during his career. He won the Buckley Prize (with D.G. Thomas), awarded by the American Physical Society, in 1969 for his work on light-emitting diodes (LEDs). He won a MacArthur grant in 1983. He was named California Scientist of the Year in 1991 and won the Albert Einstein Award of Science in 2005. Hopfield also was awarded a Guggenheim fellowship (just as his father had been, 40 years earlier) in 1968.
The trusted destination for professionals, college students, and lifelong learners.
SUBSCRIBE
